An optimized blind dual medical image watermarking framework for tamper localization and content authentication in secured telemedicine

Abstract Maintaining secured patient credentials in telemedicine is becoming a critical task. Image watermarking is one of the solutions to this problem. It is extensively used to protect and block the content alteration. Medical images may acquaint with tampers during transit in telemedicine. Before taking a prior decision about referring for diagnosis, the reliability of region of interest (ROI) of the watermarked medical test image must be tested to avoid faulty diagnosis. In this paper, tamper recognition and authenticity were obtained by concealing the dual watermarks into the region of non-interest (RONI) blocks of the medical image. These blocks are chosen by the characteristics of Human Visual System (HVS) with the integration of Discrete Wavelet Transform (DWT) and Schur transform along with the Particle Swarm Bacterial Foraging Optimization algorithm (PSBFO). The major focus of the PSBFO algorithm is to select the threshold value for obtaining optimum results in terms of imperceptibility and robustness against attacks. The dual watermarks are compressed by Lempel-Ziv-Welch (LZW) lossless compression algorithm to increase the payload capacity. Simulation outcomes conducted on different types of medical images disclose that the proposed scheme demonstrates superior transparency and robustness against signal and compression attacks compared with the related hybrid optimized algorithms. It also recognizes the existence of tampers inside the portion of ROI with 100% precision. The proposed scheme is also able to retrieve the original ROI without losing any information and provides optimum security capability when compared with the state-of-the-art algorithms.